A Systems Engineering Approach to Improving the ...

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IEEE SYSTEMS JOURNAL, VOL. 8, NO. 1, MARCH 2014

A Systems Engineering Approach to Improving the Accuracy of Mobile Station Location Estimation Reza Rahdar, Member, IEEE, Jerrell T. Stracener, and Eli V. Olinick

Abstract—Enhanced 911 is a first line of assistance for practically every emergency situation, and many cell phone users today expect the same results from an emergency call no matter where they are—whether on the side of the road, in the woods, or in a building. It is a vital part of our nation’s emergency response and disaster preparedness system. In the context of 911 service, demand for providing reliable and accurate mobile station (MS) location estimation has become a high priority and has gained momentum in recent years. A major challenge in mobile station location estimation is locating an emergency caller within desired accuracy in an adverse environment where nonline-of-site (NLOS) propagation exists. This paper develops a methodology to improve the accuracy of mobile station location estimation in an NLOS environment. A unique feature of this methodology development, compared to other approaches in the literature, is the application of the systems engineering process. While there are many definitions, systems engineering as applied here is an approach and process for developing the preferred solution to a set of requirements. The methodology consists of two stages. In the first stage, a series of time-of-arrival range measurements are made from each base station (BS) to the MS. Binary hypothesis testing on the standard deviation of the range measurements at a given BS is used to determine if the measures are taken under NLOS conditions. Then, if possible, any BS deemed to be NLOS is eliminated from the estimation in the second stage, in which the selected time measurements of several BSs are combined through least squares to estimate the location of the mobile station. Based on a simulation study, the methodology appears to have the potential to significantly improve the accuracy of location estimates in certain situations. Index Terms—Constrained least squares, least squares, lineof-site (LOS), mobile station location estimation, non-line-of-site (NLOS), root mean square error, systems engineering, vee model.

I. Introduction

T

HE use of wireless communication systems has grown explosively in recent years. According to the Cellular Telephone Industry Association (CTIA), there were approximately 233 million subscriptions for wireless service in the United States in 2006; by the end of 2011 this number had

Manuscript received January 13, 2012; revised July 13, 2012; accepted January 11 2013. Date of publication March 7, 2013; date of current version February 5, 2014. This work was supported in part by ONR under Contract N00014-10-0852. R. Rahdar is with Bell Helicopter, Fort Worth, TX 75275 USA (e-mail: [email protected].). J. T. Stracener and E. V. Olinick are with the Department of Engineering Management, Information, and Systems, Bobby B. Lyle School of Engineering, Southern Methodist University, Dallas, TX 75275 USA (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSYST.2013.2244799

grown to over 331 million [1]. Due to the fact that many Americans have multiple subscriptions, this is a wireless penetration of over 104% of the potential market [2]. In addition, CTIA reports that the percentage of wireless-only households in the U.S. has tripled from 10% in 2006 to over 30% in 2011, while the number of 911 calls per day made from wireles/cellular phones (mobile stations) has increased from 260 000 to over 400 000 in the same period [1]. Locating emergency callers in wireless communication systems—mobile station location estimation—has emerged as an essential public safety issue. Motivated by Federal Communications Commission (FCC) requirements, methodologies for improving the accuracy of mobile station location estimation have become an important research area over the past few years. A Public Safety Answering Point (PSAP), or 911 Center, is a call center responsible for answering emergency calls and dispatching appropriate services, such as police, fire fighters, and ambulances. In a wireline network with E911 (E911) service, automated number identification information gives the PSAP call back capability in case the emergency call is disconnected. E911 also enables the PSAP to identify an emergency call’s originating address through the use of an automated location identification database. In wireless communication systems, however, the mobile station (MS) has no fixed static address because it can be at any geographical location (e.g., at the owner’s home, on the road, out of city or state, or even out of the country). The 911 call from a mobile station may not be routed to the nearest 911 center, and the dispatcher may not receive the callback phone number or the location of the caller. This can lead to a disaster if the caller does not know their location or cannot communicate their location because they are unable to speak. The delivery of wireless location data that allows the receiving PSAP to promptly and effectively dispatch the appropriate emergency services to the correct location is essential to wireless 911 services. When a mobile station initiates a 911 call in a wireless network, the closest cell tower (base station) picks up the signal. The base station (BS) transmits the caller’s voice signal, phone number, and the tower’s identification code to a mobile switching center. The mobile switching center then forwards this information to the appropriate PSAP, which subsequently processes location information to dispatch local responders to locate and assist the caller. A typical flow of information in a wireless 911 call is shown in Fig. 1. Although it is possible to identify the base station closest to where a wireless call is

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RAHDAR et al.: SYSTEMS ENGINEERING APPROACH TO IMPROVING THE ACCURACY OF MOBILE STATION LOCATION ESTIMATION

Fig. 1.

Wireless 911: flow of information.

initiated, it is not yet possible to identify the exact location of a mobile station making an emergency call. In 1996, the FCC adopted regulations for wireless calls to 911 to ensure compatibility with E911 emergency calling; these wireless E911 regulations require all wireless service providers to report accurate mobile station location information to the E911 operator at the PSAP [3]. At the time, the FCC called for a phased implementation of the wireless E911 regulations. In Phase 0, wireless service providers were required to transmit any 911 call received by their network to a PSAP regardless of whether or not the caller subscribed to their service [4]. In Phase I, the service providers were obliged to provide PSAPs with the mobile phone callback number and the location of the cell tower (BS) that received the call. Knowing the location of the BS that received the 911 call gave emergency responders a rough estimate of the location of the MS; in Phase II, however, service providers were required to relay longitude and latitude of the MS to the PSAPs with the following accuracy and reliability: 1) for network-based solutions: 100 m for 67% of calls, 300 m for 95% of calls; 2) for handset-based solutions: 50 m for 67% of calls, 150 m for 95% of calls. Phase I was implemented by the end of 1998, and the regulations allowed service providers a five-year time window for implementing Phase II [3]. As noted in [5], however, “providing the [required] E911 service in a manner that is economically feasible for the carriers has proven to be quite a challenge;” Phase II has not been fully implemented and the FCC has fined several national service providers for failing to meet their requirements [5]. In September 2007, the FCC adopted a report and order to clarify Phase II location accuracy requirements. The order required carriers to meet interim and annual benchmarks over the next five years and achieve full compliance with the E911 regulations by September 11, 2012 [6]. In a 2010 report, the FCC noted that as many as 40% of emergency calls made from wireless devices fail to provide accurate mobile station location estimation [7]. Present wireless systems are still unable to provide PSAPs with mobile station

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location estimates that meet the FCC requirements. There are numerous national stories, sometimes ending tragically, highlighting the inability of wireless E911 to locate people. Pinpointing the location of mobile stations presents some unique challenges due to the hostile effects of the wireless environment such as noise, electromagnetic interference, multipath, non-line-of-sight (NLOS) propagation, shadow fading, and Doppler shifts. In most situations the MS is not in direct path line-of-sight (LOS) with the BS. With no LOS path, the transmitted signal can only reach the receiver through reflection, diffraction, or scattering. In this case, the signal takes a longer time to reach the BS than it would with a LOS path. This delay is added to the distance measurement as an additional error that is called NLOS error. This additional error may be large and thus may cause the mobile station location estimate to be far from the true location. The field trials performed by Woo et al. [8] showed that the location distance error due to NLOS paths can be more than 500 m. Obstacles that commonly cause NLOS errors include buildings, trees, hills, and mountains. Extensive studies have been carried out to mitigate NLOS effects but none adequately achieved the accuracy required by the FCC, and our survey of the literature indicates that improvement is needed. This paper presents a methodology to improve the accuracy of network-based mobile station location estimation in a NLOS environment. The methodology consists of two stages. In the first stage, a series of time of arrival (TOA) range measurements are made from each BS to the MS. Binary hypothesis testing on the standard deviation of the range measurements at a given BS is used to determine if the measures are taken under NLOS conditions. Then, if possible, any BS deemed to be NLOS is eliminated from the estimation in the second stage, in which the selected time measurements of several BSs are combined through least squares to estimate the location of the mobile station. The remainder of this paper is organized as follows. Section II is an overview of the Vee model of the systems engineering process used to develop the methodology described above. The application of the Vee model to improving mobile station location estimation is demonstrated in Sections III through VI. The requirements for the methodology are defined in Section III. Section IV includes a short discussion of techniques for mobile station location estimation and a brief survey of the NLOS error mitigation literature. Our mobile station location estimation methodology is developed in Section V, and Section VI summarizes the results of a simulation study used to test its performance. Conclusions from the study are given in Section VII. II. Systems Engineering Process In this paper, the systems engineering process is used to develop a methodology for improving the accuracy of networkbased mobile station location estimation. While there are many definitions, systems engineering as applied here is an approach and process for developing the preferred solution to a set of requirements. This process usually consists of the following seven tasks: 1) state the problem; 2) investigate alternatives;

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Fig. 2.

IEEE SYSTEMS JOURNAL, VOL. 8, NO. 1, MARCH 2014

Systems engineering process.

best technique and down select to the best alternative. This is followed by the methodology development described in Section V. The right side of the Vee for this application (verification and validation of the methodology developed in Section V) is described in Section VI. III. Needs and Requirements

Fig. 3.

SE Vee model.

3) model the system; 4) integrate; 5) launch the system; 6) assess performance; and 7) re-evaluate (SIMILAR). This systems engineering process is shown in Fig. 2, which is adapted from [9]. The systems engineering Vee model was selected to provide the framework for development of an improved methodology. The objective was to ensure capture of requirements, evaluate the different methods in the literature, to develop the best solution (methodology) to meet the established requirements and verify and validate it. The Vee model was developed by Kevin Forsberg and Harold Mooz at the Center for Systems Management [10]. Since then, it has been refined and applied in many different industries. The Vee model is a system development model designed to simplify the understanding of the complexity associated with developing systems [11]. The Vee shape is derived by the concept of an evolving engineering process, moving from left to right with time where the left side of the Vee depicts the decomposition and definition of the system requirements and specifications at the beginning of the system’s life cycle. After the system or product is developed, responsibility passes back to the right side of the Vee for integration and verification. Ultimately, the completed system is validated to measure how well it meets the user’s needs. The adapted Vee model shown in Fig. 3 captures the process of developing a methodology for locating a mobile station, then verifying and validating it to satisfy the requirements. In the following sections, the general principles of the Vee model are used in developing the new methodology for improved mobile station location estimation. Our application of the Vee model for the E911 application is described more thoroughly in [12]. The process begins with the customer needs and requirements on the upper left side of the Vee with decomposition and definition activities as described in Section III. The next step on the Vee (Section IV) summarizes the literature survey and trade studies used to establish the

Emergency 911 service is a vital part of our nation’s emergency response and disaster preparedness system. Finding the location of mobile stations has emerged as an essential public safety feature of cellular systems. To comply with FCC regulations, wireless carriers will have to ensure E911 coverage for 95% of their subscriber base at each PSAP [3], [6]. The accuracy of different mobile station location estimation techniques has been studied, but their accuracy is often inadequate and unreliable for many location based services. Although the complete removal of the NLOS impact may be impractical, different techniques can be used to mitigate its adverse effect and thereby improve accuracy of mobile station location estimation. Based on the FCC mandate and the wireless technology performance, the following requirements were derived with each one being measurable and verifiable: 1) the methodology shall address the greatest impairment to the mobile station location estimation; 2) the methodology shall identify and mitigate the NLOS effects; 3) the methodology shall be comparable with the best alternatives; 4) the methodology shall meet the FCC mandates. IV. Alternatives Rahdar [12] gives a detailed discussion of the investigation and selection of alternatives for mobile station location estimation. The investigation has two main parts: selecting a technology for estimating the mobile station location, and then improving the implementation of that technology with a method for mitigating NLOS errors. A. Mobile Station Location Estimation There are a number of technological alternatives for locating the mobile station. The location technologies are typically grouped into two main categories: handset-based and networkbased. In handset-based systems, the MS determines its location from signals received from nearby BSs or from the global positioning system (GPS). In network-based methods,

RAHDAR et al.: SYSTEMS ENGINEERING APPROACH TO IMPROVING THE ACCURACY OF MOBILE STATION LOCATION ESTIMATION

the network infrastructure is used to identify the location of the mobile station. Two arguments in favor of handset-based systems are that commercial/civilian-grade GPS can be extremely accurate under optimal conditions—hence the more stringent FCC accuracy requirements—and that cell phones with GPS capability are becoming increasingly prevalent as evidenced by Apple’s report that it sold over 37 million GPS-enabled iPhones in the final quarter of 2011 [14]. However, there are still a significant number of cell phones in use that do not support GPS, and even when the vast majority of wireless users have GPS-enabled phones there will still be a need for network-based solutions as the accuracy of handset-based technology such as GPS can be reduced by atmospheric conditions, terrain types, and in urban/indoor settings [15], [16]. The advantage of network-based methods is that they can be implemented non intrusively without affecting the mobile handsets. The accuracy of network-based methods varies, with cell identification as the least accurate and triangulation/trilateralization as the most accurate. The accuracy of network-based methods is closely dependent on the concentration of base station cells, with urban environments achieving the highest possible accuracy [17]. After surveying a variety of network-based methods, such as TOA (see Section V-A), time difference of arrival [18], cell identification [19], received signal strength indicator [20], angle of arrival [21], and hybrid methods (e.g., [22], [23]), Rahdar [12] selects TOA as the basic technology for mobile station location estimation. B. NLOS Mitigation Several approaches to identify and mitigate NLOS errors in mobile station location estimation have been studied in recent years. Wylie and Holtzman [24] showed that it is possible to distinguish LOS from NLOS measurements at each BS by using the time history of the range measurements in a hypothesis testing method. Likewise, Borr`as et al. [25] formulate the NLOS identification problem as a binary hypothesis test where the range measurements are modeled as being corrupted by additive noise with various probability distributions. They solve the binary hypothesis test under several assumptions and propose decision criteria for determining if a BS is LOS or NLOS. Chen [26] developed a residual-based algorithm to mitigate the NLOS errors in mobile station location estimation. This method was also used in [27]–[29]. The residual-based method relies on a large number of measurements that are grouped into subsets. Intermediate location estimates are produced from each subset of the measurements and are evaluated by their residuals [26], [29]–[31]. The final location estimate is obtained by weighting the different intermediate results. The method is effective when only a few measurements are corrupted by NLOS effects. Studies such as [24], [25], [28], [31]–[34] apply statistical parameter estimation of the NLOS error to solve the mobile station location estimation problem. These methods depend largely on the accuracy of the NLOS error model, and satisfactory location accuracy is expected if the model and distribution parameters can be well tuned on site [35]. Methods exploiting mathematical optimization models, often with geometrical constraints, have been proposed in

Fig. 4.

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TOA location estimation using three LOS BSs.

[35]–[42]. A variety of filtering-related methods have been proposed (e.g., [43]–[45]) to reduce the NLOS effect and to track moving targets by using motion dynamics.

V. Methodology Development Our mobile station location estimation methodology builds upon several concepts from the literature. In Section V-A, we describe the TOA measurement method for calculating the distance (range) between the MS and a BS, and determining the location of the MS. In Sections V-B and V-C, we describe methods proposed in the literature for using the TOA measurement method in the presence of measurement error and a simple hypothesis testing method for deciding if a BS is LOS or NLOS, respectively. We then describe our new methodology in subsection V-D. Throughout this paper we assume that there are N BSs, whose known 2-D locations are denoted by (xi , yi ), i = 1, 2, . . . , N, within range to receive a 911 signal from a stationary MS whose location is to be determined and whose unknown coordinates are denoted by (x, y). A. TOA Measurement Method The TOA measurement method is used to estimate the distance (range) between the MS and a BS. Since the wireless signal travels at the speed of light (c = 3 × 108 m/s), the estimated distance between the MS and BSi is ri = c(ti − t0 )

(1)

where t0 is the time instant at which the MS initiates the call and ti is the time of arrival of the signal at BSi . Using the estimated distance from the MS to three BSs, the location of the MS is then determined using trilateralization as illustrated in Fig. 4. B. Mobile Station Location Estimation in the Presence of Measurement Error The true distance between the MS and BSi is given by di = ((x, y) − (xi , yi )) =

 (x − xi )2 + (y − yi )2 .

(2)

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IEEE SYSTEMS JOURNAL, VOL. 8, NO. 1, MARCH 2014

TABLE I

Under ideal circumstances (2) holds and ri = di . In practice, however, there is some measurement error even when a BS has a line of sight with the mobile station. Thus, ri is typically modeled as an unbiased Gaussian estimate of the true distance  ri = di + ni = (x − xi )2 + (y − yi )2 + ni (3) where ni is the measurement noise and is a zero-mean Gaussian random variable with a standard deviation of σ in the range of 60 m to 150 m [24]. Note that in this paper, we model measurement noise with a variety of probability distributions. In the case of NLOS, the measurement noise and a NLOS error both exist and the distance estimates ri are known to be positively biased estimates of the true distance [26] given by  ri = (x − xi )2 + (y − yi )2 + ni + εi (4) where εi is the NLOS error. The standard deviation of a series of M TOA distance measurements from the MS to BSi is denoted by si and calculated as   M  1  si =  (rij − r¯i )2 (5) M − 1 j=1 where rij is the jth measurement in the series and r¯i is the mean of the M measurements that is calculated as r¯i =

M 1  rij . M j=1

(6)

To describe the least squares (LS) algorithm proposed by Cheung et al. [46] for mobile station location  estimation in the presence of measurement error, let R = x2 + y2 and  = T  x y R2 .  can be estimated using LS = argmin (A − b)T (A − b) = (AT A)−1 AT b  where

⎡

y1 y2 .. .

x1 ⎢ x2 ⎢ A=⎢ . ⎣ ..

⎤ −0.5 −0.5⎥ ⎥ .. ⎥ . ⎦

yN −0.5 ⎤ x =⎣ y ⎦ R2 ⎤ ⎡ x1 + y1 − r¯12 ⎥ 1⎢ ⎢ x2 + y2 − r¯2 ⎥ b= ⎢ ⎥. .. ⎦ 2⎣ . 2 xN + yN − r¯N xN

(7)

(8)

⎡

(9)

(10)

Accept H0 Reject H0

H0 is true Correct decision Type I error

H0 is false Type II error Correct decision

subject to qT  + T P = 0 where

⎡

1 P = ⎣0 0 ⎡

0 1 0 0 q=⎣ 0 −1

(12)

⎤ 0 0⎦ 0 ⎤

(13)

⎦.

(14)

The weighting matrix for (11) is W = (BQB)−1 where B = 2 diag (2¯r1 , 2¯r2 , . . . , 2¯rN ) and Q = diag (s12 , s22 , . . . , sN ). C. LOS/NLOS Identification NLOS errors can severely degrade mobile station location estimation accuracy. Therefore, if possible, they should be removed from any mobile station location estimation procedure. If three of the N BSs can be identified as LOS, the MS location can be determined using TOA range estimates and the LS algorithm described above without the need to mitigate the NLOS error. In this section we describe a methodology proposed by Wylie and Holtzman [24] to discriminate between LOS versus NLOS measurement. The LOS/NLOS identification problem can be treated as a binary hypothesis testing problem exploiting the fact that the standard deviation of NLOS errors is greater than that of the LOS errors [24]. That is, if BSi is NLOS, then it is likely in a sufficiently large series of TOA measurements that si will be larger than σ; where σ is the standard deviation of the LOS errors. This hypothesis testing problem can be stated as follows. The null hypothesis, denoted by H0 , is that the BS is LOS, and the alternative hypothesis, denoted by H1 , is that it is NLOS. Given a set of M TOA measurements at BSi , the hypothesis test deems the BS to be LOS if si ≤ σ. Otherwise, if si is above the threshold value σ, the hypothesis test deems the BS to be NLOS. In this test the null hypothesis, H0 , will be rejected in favor of H1 for relatively large values of si . A type I error is committed when H0 is rejected when, in fact, it is true. A type II error occurs when H0 is wrongly accepted when it is false. There errors are summarized in Table I. D. New Mobile Station Location Estimation Methodology

Cheung et al. [46] also describe a constrained weighted least squares (CWLS) algorithm in which a weighting matrix W is added to (7) to improve performance = argminθ (A − b)T W(A − b) 

Type I and Type II Errors

(11)

Our new mobile station location estimation (MSLE) Methodology may be described as the following two-stage procedure. Stage 1: Take a series of M TOA distance measurements from the MS to BSi , apply the hypothesis test described above and designate BSi as LOS or NLOS accordingly. Repeat this

RAHDAR et al.: SYSTEMS ENGINEERING APPROACH TO IMPROVING THE ACCURACY OF MOBILE STATION LOCATION ESTIMATION

process at each of the other BSs until one of two stopping criteria are met: 1) three BSs have been designated as LOS (i.e., as soon as trilateralization can be performed with a high degree of confidence), and 2) distance measurements have been taken, and hypothesis tests have been performed, for all N BSs. Stage 2: If Stage 1 ends due to criterion 1, then apply LS to the distance measurement averages from the three BSs designated as LOS to estimate the location of the MS. Otherwise, apply LS to the distance measurement averages from the three BSs whose M distance measurements in Stage 1 had the smallest standard deviations to estimate the location of the MS. Intuitively, the methodology can be described as estimating the MS location with measurements taken from the first three BSs that appear (based on the hypothesis test) to have a LOS with the MS or, if there are fewer than three such BSs, measurements from any BSs that appear to have a LOS with the MS and the best NLOS BSs.

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TABLE II Simulated Hypothesis Test Results with 2 NLOS BS

BSi 1 2 3 4 5

LOS/NLOS LOS NLOS LOS NLOS LOS

Mean si 60.59 294.97 60.59 294.78 60.65

# of Errors Type I Type II 371 — — 0 385 — — 0 386 —

TABLE III Simulated Hypothesis Test Results with 5 NLOS BS BS (i) 1 2 3 4 5

LOS/NLOS NLOS NLOS NLOS NLOS NLOS

Mean si 294.90 294.91 294.78 295.08 295.22

# of Type II Errors 0 0 0 0 0

B. Simulation Results VI. Verification and Validation A. Simulation Environment Monte Carlo simulations using MATLAB were used to evaluate the performance of the MSLE methodology. Following a frequently cited example from the literature, [47], a 5-cell 2-D cellular system was used. The base stations √ are located at√the following points in the plane: BS = (4 3, 12), BS √ √1 √2 = (4 3, BS4 = (8 3, 8), BS5 = (5 3, 4). The 20), BS3 = (8 3, 16), √ MS was located at (6 3, 8). Note that x–y coordinates are given in kilometers. For these simulations, the measurement noise ni was modeled using three different types of probability distributions, namely, the uniform, normal, and triangular. The uniform and normal probability distributions are frequently used in the mobile station location estimation literature. The triangular distribution is defined by three parameters, [min, mode, max], where min, mode, and max are the smallest, most frequent, and largest values, respectively. It is often used in cases where data is limited, or difficult and/or expensive to collect [48]. Each simulation experiment consisted of 1000 independent runs. In simulations using the uniform distribution, the measurement noise ni was selected to be over the range from zero to 200 m for all five base stations. Thus, the LOS/NLOS threshold for the hypothesis test was about σ = 60 m which is approximately the standard deviation of the uniform distribution on [0, 200]. When using the normal distribution, the measurement noise ni was set to have a mean of 100m and standard deviation of 50m for all base stations. Thus, the LOS/NLOS threshold for the hypothesis test was σ = 50 m. For the triangular distribution ni was set to be over the range from zero to 200 m for all base stations with a mode of 100m. The triangular distribution [0, 100, 200] has a mean of 100 and standard deviation of approximately 41, and so the LOS/NLOS threshold for the hypothesis test in these simulations was about σ = 41 m. In all simulations, the NLOS error εi for any NLOS BS was modeled using the uniform distribution on the range from zero to 1300 m.

To evaluate the potential for the hypothesis test to correctly make LOS/NLOS determinations in the simulation environment described above we made 1,000 simulation runs in which BSs 1, 3, and 5 were LOS with respect to the MS and BSs 2 and 4 were NLOS. In each run, M = 500 distance measurements were simulated at each BS. For each BS, Table II shows the average value of si (in meters) and the number times the hypothesis test correctly designated the BS as LOS or NLOS over the 1000 runs. Observe that in this experiment the hypothesis test made the correct LOS/NLOS designation over 60% of the time for BSs with LOS and 100% of the time for BSs with NLOS. Table III shows results from a similar experiment in which all five BSs were NLOS. Based on the results in Tables II and III, we conclude that it is possible to distinguish between LOS and NLOS measurements using the hypothesis test in the simulation environment described above. 1) Mobile Station Location Estimation Accuracy: Tables IV–VI summarize the results of our simulation runs. For these runs we considered seven different cases. In cases 1, 2, and 3, a single simulation run consisted of randomly selecting a subset of four out of the five BSs, randomly selecting a subset of those four BSs to be NLOS, simulating M = 500 distance measurements at each of the four BSs, and then applying three mobile station location estimation algorithms: LS with all four BSs, CWLS with all four BSs, and our MSLE methodology. In cases 4, 5, 6, and 7, a single simulation run consisted of randomly selecting a subset of the five BSs to be NLOS, simulating M = 500 distance measurements at each of the five BSs, and then applying the three mobile station location estimation algorithms mentioned above. For a given location estimate, we used the root mean square error (RMSE) as the measure of goodness. In Tables IV–VI, we list the percentage of RMSEs that were within the allowable FCC limits. As demonstrated in Tables IV–VI, the MSLE methodology outperformed the two other algorithms in all but three cases: case 1 (4 BS/0 NLOS), case 4 (5 BS/0 NLOS) and case 7 (5 BS/3 NLOS). Table VII summarizes the MSLE methodology’s

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IEEE SYSTEMS JOURNAL, VOL. 8, NO. 1, MARCH 2014

TABLE IV

TABLE VII

Performance Comparison of Algorithms With Uniform Distribution for LOS errors

MSLE Performance Comparison With Three Distributions

LS #of BS/NLOS 4/0 4/1 4/2 5/0 5/1 5/2 5/3

%in 100 m 100 0 12 100 0 12 0

%in 300 m 100 56 25 100 60 22 18

RMSE CWLS %in %in 100 m 300 m 100 100 9 37 11 12 100 100 0 40 0 10 0 9

MSLE %in % in 100 m 300 m 76 100 72 99 71 99 90 91 87 91 86 89 0 16

TABLE V Performance Comparison of Algorithms With Normal Distribution for LOS Errors

LS #of BS/NLOS 4/0 4/1 4/2 5/0 5/1 5/2 5/3

%in 100 m 100 0 10 100 0 10 0

%in 300 m 100 60 24 100 60 22 22

RMSE CWLS %in %in 100 m 300 m 100 100 7 39 4 10 100 100 0 41 0 11 2 9

MSLE %in %in 100 m 300 m 75 100 71 100 70 100 90 93 90 91 91 92 0 3

TABLE VI Performance Comparison of Algorithms in Five-Cell Simulations With Triangular Distribution for LOS Errors

LS #of BS/NLOS 4/0 4/1 4/2 5/0 5/1 5/2 5/3

%in 100 m 100 0 10 100 0 10 0

%in 300 m 100 62 28 100 62 20 19

RMSE CWLS %in %in 100 m 300 m 100 100 2 40 0 10 100 100 0 42 0 10 0 15

MSLE %in % in 100 m 300 m 100 100 100 100 71 100 100 100 100 100 89 90 0 3

performance with the three different distributions for the measurement noise. For the uniform probability distribution, the calculated RMSEs are within 100 m and 300 m more than 71% of time; exceeding 67% as required by FCC. For the normal probability distribution, the calculated RMSEs are within 100 m and 300 m over 69% of time and above 67% as required by FCC. Also, for the triangular probability distribution, the calculated RMSEs are within 100 m and 300 m over 74% of the time, which is also above 67% as required by FCC. In cases where all base stations had a LOS path with the MS (case 1 and case 4), the RMSEs of the location estimates from the LS and CWLS procedures were always within 100 m, whereas those from the MSLE methodology were within 100 m 76% of the time in case 1 and 90% in case 4 when

#of BS/NLOS 4/0 4/1 4/2 5/0 5/1 5/2 5/3

Uniform %in %in 100m 300m 76 100 72 99 71 99 90 91 87 91 86 89 0 16

RMSE Normal %in %in 100m 300m 75 100 71 100 70 100 90 93 90 91 91 92 0 3

Triangular %in % in 100m 300m 100 100 100 100 71 100 100 100 100 100 89 90 0 3

the measurement errors were generated from the uniform distribution, and within 100 m 75% of the time in case 1 and 90% of the time in case 4 when the errors were generated from the normal distribution. These results suggest a refinement to the MSLE methodology that might yield improved results in cases where all BSs have an LOS path with the MS. 1) Ignore the first stopping criterion in Stage 1 and instead take TOA measurements from all N BSs. 2) Stage 2: a) Let N  be the number of BSs deemed by the hypothesis test to be LOS. b) If N  ≥ 3, then apply LS using all N  LOS BSs. c) If N  < 3, then apply LS using all N  LOS BSs and the 3 − N  NLOS BSs with the smallest standard deviations in their TOA measurements. Note, however, that one of the appealing features of the MSLE methodology, aside from improved accuracy, is that it can save valuable time by stopping TOA measurements as soon as three BSs are deemed to have LOS with the MS. When N  is larger than 3, the methodology described above requires extra time to take the range measurements at the additional N  −3 BSs; each additional LOS BS used in the estimation adds an equation to the LS problem (7), which means it could take longer to perform the calculations to estimate the location of the MS. Although the wireless E911 regulations do not have specific time limits for the mobile station location estimation process, it is clearly important that the PSAP receive the most accurate estimate possible in as short a time as possible. Thus, a tradeoff between processing time and accuracy must be made when deciding which version of the MSLE methodology to use. VII. Conclusion This paper presented a methodology to improve the accuracy of mobile station location estimation in an NLOS environment. The methodology built on methods previously presented in the literature, involving hypothesis testing to determine which BSs were LOS, and which were NLOS and the use of least squares to estimate MS location. A unique feature of this methodology development, compared to other approaches in the literature, was the application of the systems engineering process to provide a framework for evaluating previously proposed methods and to guide development of an improved methodology to meet established requirements.

RAHDAR et al.: SYSTEMS ENGINEERING APPROACH TO IMPROVING THE ACCURACY OF MOBILE STATION LOCATION ESTIMATION

Based on a simulation study, the methodology appears to have potential for significantly improving the LS location estimate in certain situations. A large-scale simulation study to characterize these situations is the subject of an on-going investigation. It was important to note that our methodology was designed for network-based solutions. It seemed likely that some time within in the next decade the FCC will require all wireless service providers to meet the more stringent requirements that are presently mandated only for handset-based systems [15] [49]. Thus, we anticipated a need to develop mobile station location estimation methodologies for hybrid systems of GPSbased and network-based technologies in the near future. References [1] CTIA. (2012, Jul.) Wireless Quick Facts [Online]. Available: http://www.ctia.org/advocacy/research/index.cfm/aid/10323 [2] A. Cocotas. (Mar. 29, 2012). There Are More U.S. Wireless Subscriptions Than There Are Americans [Online]. Available: h ttp://articles.businessinsider.com/2012-0329/news/31253083 1 smartphones-subscriptions-post-pc-era [3] FCC. (1996). “Revision of the commissions rules to ensure compatibility with enhanced 911 emergency calling systems,” docket 94-102, Tech. Rep. RM-8143 [Online]. Available: http://transition.fcc. gov/Bureaus/Wireless/Orders/1996/fcc96264.txt [4] FCC. (2001). FCC Wireless 911 Requirements [Online]. Available: http://transition.fcc.gov/pshs/services/911-services/enhanced911/ archives/factsheet requirements 012001.pdf [5] Spread Spectrum Scene Online, Inc. (2008, Sep. 8.). Enhanced 911 (E911) Position Location Info [Online]. Available: http://sssmag.com/e911.html [6] Directions Media. (2007, Sep.) FCC Clarifies Geographic Area Over Which Wireless Carriers Must Meet Enhanced 911 Location Accuracy Requirements [Online]. Available: http://www.directionsmag.com/ pressreleases/fcc-clarifies-geographic-area-over-which-wireless-carriersmust-meet-enhanc/112599 [7] FCC. (2010, Sep.) FCC Takes Action to Improve Wireless 9-11 Servcies [Online]. Available: http://fjallfoss.fcc.gov/edocs public/ attachmatch/DOC-301653A1.pdf [8] S. S. Woo, H. R. You, and J. S. Koh, “The NLOS mitigation technique for position location using IS-95 CDMA networks,” in Proc. 52nd IEEE VTS, vol. 6. Sep. 2000, pp. 2556–2560. [9] A. T. Bahill and B. Gissing, “Re-evaluating systems engineering concepts using systems thinking,” IEEE Trans. Syst., Man, Cybern., C Appl. Rev., vol. 28, no. 4, pp. 516–527, Nov. 1998. [10] The Center for Systems Management (CSM). (2007). Visual Process Models [Online]. Available: http://www.csm.com/repository/ site/SitePage-VisualModels.aspx [11] International Council on Systems Engineering (INCOSE). (2003). Guide to the Systems Engineering Body of Knowledge (G2SEBoK) [Online]. Available: http://g2sebok.incose.org/ [12] R. Rahdar, “A systems engineering methodology for improving the accuracy of mobile station location estimation,” Ph.D. dissertation, Southern Methodist Univ., Dallas, TX, USA, Dec. 2010. [13] Garmin, Ltd. (2012, Jul.). What Is GPS [Online]. Available: http://www8.garmin.com/aboutGPS/ [14] D. Rushe. (2012, Jan. 24). Apple Announces Record Sales of iPhones and iPads [Online]. Available: http://www.guardian.co.uk/technology/ 2012/jan/25/apple-annnounce-record-sales-iphones-ipads [15] D. Seifert. (2011). FCC to Require GPS in All Phones by 2018 [Online]. Available: http://www.mobileburn.com/16915/news/fcc-torequire-gps-in-all-phones-by-2018 [16] FCC. (2010). Second Report and Order (Order) in PS Docket No. 07-114, FCC 10-176 [Online]. Available: https://www.federalregister. gov/articles/2010/11/18/2010-29007/wireless-e911-location-accuracyrequirements [17] Wikipedia. (2010, Mar.). CDMA-Code Division Multiple Access http://en.wikipedia.org/wiki/File:UMTSNetworkArchitecture [18] L. Cong and W. Zhuang, “Non-line-of-sight error mitigation in TDOA mobile location,” in Proc. IEEE GLOBECOM, vol. 1. Nov. 2001, pp. 680–684.

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[19] K. Raja, Critical Analysis and Modeling of Location Finding Services, ECFRC, vol. 4, Jul. 2004. [20] A. A. Moghaddam, “Enhanced cellular network positioning using spacetime diversity,” Ph.D. thesis, Univ. Calgary, Calgary, Canada, Dec. 2007. [21] R. Kumaresan and R. Tufts, “Estimating the angels of arrival of multiple plane waves,” IEEE Trans. Aerospace Electron. Syst., vol. 19, no. 1, pp. 134–139, Jan. 1983. [22] L. Cong and W. Zhuang, “Hybrid TDOA/AOA mobile user location in wideband CDMA systems,” in Proc. 3rd IEEE Int. Conf. Generation Wireless Commun., Jun. 2000, pp. 648–655. [23] N. J. Thomas, D. G. M. Cruickshank, and D. I. Laurenson “Performance of a TDOA-AOA hybrid mobile location system,” in Proc. IEEE 3G2001 Conf., Mar. 2001, pp. 24–25. [24] M. P. Wylie and J. Holtzman, “The non-line of sight problem in mobile location estimation,” in Proc. 5th IEEE Int. Conf. Universal Personal Commun., vol. 2. Sep.–Oct. 1996, pp. 827–831. [25] J. Borr`as, P. Hatrack, and N. B. Manclayam, “Decision theoretic framework for NLOS identification,” in Proc. IEEE VTC, Apr. 1998, pp. 1583–1587. [26] P. C. Chen, “A non-line-of-sight error mitigation algorithm in location estimation,” in Proc. IEEE WCNC, vol. 1. Sep. 1999, pp. 316–320, [27] L. Cong and W. Zhuang, “Non-line-of-sight error mitigation in TDOA mobile location,” in Proc. IEEE GLOBECOM, vol. 1. Nov. 2001, pp. 680–684. [28] S. Venkatraman, J. Jr. Caffery, H. R. You, “A novel TOA location algorithm using LOS range estimation for NLOS environments,” IEEE Trans. Vehic. Technol., vol. 53, no. 5, pp. 1515–1524, Sep. 2004. [29] Y. T. Chan, W. Y. Tsui, H. C. So, and P. C. Ching, “Time-of-arrival based localization under NLOS conditions,” IEEE Trans. Vehic. Technol., vol. 55, no. 1, pp. 17–24, Jan. 2007. [30] L. Xiong, “A selective model to suppress NLOS signals in angleof-arrival (AOA) location estimation,” in Proc. 9th IEEE Int. Symp. Personal Indoor Mobile Radio Commun., Sep. 1998, pp. 461–465. [31] L. Cong and W. Zhuang, “Nonline-of-sight error mitigation in mobile location wireless communications,” IEEE Trans. Wireless Commun., vol. 4, no. 2, pp. 560–573, Mar. 2005. [32] S. Venkatraman and J. J. Caffery, “A statistical approach to non-lineof-sight BS identification,” in Proc. 5th Wireless Personal Multimedia Commun., 2002. [33] K. T. Lay and W. K. Chao, “Mobile positioning based on TOA/TSOA/TDOA measurements with NLOS error reduction,” in Proc. Int. Symp. Intell. Signal Process. Commun. Syst., Dec. 2005. [34] W. K. Chao and K. T. Lay, “NLOS measurement identification for mobile positioning in wireless cellular systems,” in Proc. IEEE VTC, Oct. 2007, pp. 1965–1969. [35] K. Yu and Y. J. Guo, “Improved positioning algorithms for nonlineof-sight environments,” IEEE Trans. Vehic. Technol., vol. 57, no. 4, pp. 2342–2353, Jul. 2008. [36] S. Al-Jazzar, J. J. Caffery, and H. R. You, “A scattering model based approach to NLOS mitigation in TOA location systems,” in Proc. 55th IEEE VTC, vol. 2. May 2002, pp. 861–865. [37] J. J. Caffery and G. L. Stuber, “Overview of radiolocation in CDMA cellular systems,” IEEE Commun. Mag., vol. 36, no. 4, pp. 38–45, Apr. 1998. [38] X. Wang, Z. Wang, and B. O’Dea, “A TOA-based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation,” IEEE Trans. Vehic. Technol., vol. 52, no. 1, pp. 112–116, Jan. 2003. [39] S. Al-Jazzar, “Algorithms and parameter estimation for radiolocation in NLOS environments,” Ph.D. thesis, Univ. Cincinnati, Cincinnati, OH, USA, May 2004. [40] K. W. Cheung, H. C. So, W. K. Ma, and Y. T. Chan, “A constrained least squares approach to mobile positioning: Algorithms and optimality,” EURASIP J. Appl. Signal Process., vol. 2006, pp. 150–155, Jan. 2006. [41] C. H. Lin, J. Y. Cheng, and C. N. Wu, “Mobile location estimation by density-based clustering for NLoS environments,” in Proc. 20th Int. Conf. AINA, 2006. [42] S. Venkatesh and R. M. Buehrer, “NLOS mitigation using linear programming in ultrawideband location-aware networks,” IEEE Trans. Vehic. Technol., vol. 56, no. 5, pp. 3192–3198, Sep. 2007. [43] S. J. Julier and J. K. Uhlmann, “A new extension of the Kalman filter to nonlinear systems,” in Proc. 11th Symp. Aerospace/Defence Sensing Simulation Controls, 1997. [44] S. J. Julier and J. K. Uhlmann, “Unscented filtering and nonlinear estimation,” Proc. IEEE, vol. 92, no. 3, pp. 401–422, Mar. 2004. [45] J. M. Huerta and J. Vidal, “Mobile tracking using ukf timing measures and LOS-NLOS expert knowledge,” in Proc. IEEE ICASSP, vol. 4. Mar. 2005, pp. 901–904.

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[46] K. W. Cheung, H. C. So, and W.-K. Ma, and Y. T. Chan, “Least squares algorithms for time-of-arrival-based mobile location,” IEEE Trans. Signal Process., vol. 52, no. 4, pp. 1121–1130, Apr. 2004. [47] M. B. Zeytinci and F. Alimoglu, “A combined approach for NLOS mitigation in cellular positioning with TOA measurements,” in Proc. EUSIPCO, 2007, pp. 530–534. [48] Brighton Webs. (2011, Oct.) [Online]. Available: http://www.brightonwebs.co.uk/distributions/triangular.htm [49] K. Dennehy. (2011). FCC Says It Isn’t Requiring All Mobiles Be Equipped With GPS by 2018 [Online]. Available: http://www.gpsworld. com/lbs/news/fcc-says-it-isn-t-requiring-all-mobiles-be-equipped-withgps-2018-12205,

Reza Rahdar (M’01) received the B.S. degree in electrical engineering from Concordia University, Montreal, QC, Canada, in 1988, and the M.S. degrees in operations research in 2003 and in systems engineering in 2006 from Southern Methodist University (SMU), Dallas, TX, USA, and the Ph.D. degree with a major in applied science, concentration in systems engineering from SMU in 2010. He is currently a Senior Engineering Specialist (Systems Engineering) with Bell Helicopter, Fort Worth, TX, USA, and an Adjunct Professor of systems engineering with Embry-Riddle Aeronautical University, Daytona Beach, FL, USA.

IEEE SYSTEMS JOURNAL, VOL. 8, NO. 1, MARCH 2014

Jerrell T. Stracener received the B.S. degree from the Arlington State College, in 1965, and the M.S. and Ph.D. degrees from Southern Methodist University, Dallas, TX, USA, in 1969 and 1973, respectively. He is the Founding Director of the Southern Methodist University (SMU) Systems Engineering Program and also teaches graduate-level courses in systems analysis methods and applications, and directs and conducts systems engineering research. He is the SMU Lead Senior Researcher with the Systems Engineering Research Center, the first University Affiliated Research Center funded by the Department of Defense (DOD) to focus on challenging systems engineering issues facing the DOD and related defense industries. He was the Co-Founder and Leader of the SAE Reliability, Maintainability, and Supportability Division (G-11).

Eli V. Olinick received the B.S. degree in applied mathematics from Brown University, Providence, RI, USA, and the M.S. and Ph.D. degrees in industrial engineering and operations research from the University of California, Berkeley, CA, USA, in 1994 and 1999, respectively. He is currently an Associate Professor with the Department of Engineering Management, Information, and Systems, Bobby B. Lyle School of Engineering, Southern Methodist University, Dallas, TX, USA. His current research interests include applied optimization, especially network design problems. Dr. Olinick is the Past Chair of the INFORMS Technical Section on Telecommunications.